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首页> 外文期刊>Computers and Electronics in Agriculture >Application of support vector machine technology for weed and nitrogen stress detection in corn
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Application of support vector machine technology for weed and nitrogen stress detection in corn

机译:支持向量机技术在玉米杂草和氮素胁迫检测中的应用。

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摘要

This study was conducted to evaluate the usefulness of a new method in artificial intelligence, the support vector machine (S VM), as a tool for classifying hyperspectral images taken over a corn (Zea mays L.) field. The classification was performed with respect to nitrogen application rates and weed management practices, and the classification accuracy was compared with those obtained by an artificial neural network (ANN) model on the same data. The field experiment consisted of three nitrogen application rates and four weed management strategies. A hyperspectral image was obtained with a 72-waveband Compact Airborne Spectrographic Imager, at an early growth stage dunng the year 2000 growing season. Nitrogen application rates were 60, 120, and 250kgN/ha. Weed controls were: none, control of grasses, control of broadleaf weeds, and full weed control. Classification accuracy was evaluated for three cases: combinations of nitrogen application rates and weed infestation levels, nitrogen application rates alone, and weed controls alone. The SVM method resulted in very low misclassification rates, as compared to the ANN approach for all the three cases. Detection of stresses in early crop growth stage using the SVM method could aid in effective early application of site-specific remedies to timely in-season interventions
机译:进行了这项研究,以评估一种新的人工智能方法,即支持向量机(SVM),作为对玉米(Zea mays L.)田间拍摄的高光谱图像进行分类的工具的实用性。根据氮肥施用量和杂草管理实践进行分类,并将分类精度与通过人工神经网络(ANN)模型在相同数据上获得的分类精度进行比较。田间试验由三种氮肥施用量和四种杂草治理策略组成。在2000年生长季节的早期生长阶段,使用72波段紧凑型机载光谱成像仪获得了高光谱图像。施氮量为60、120和250kgN / ha。杂草防治措施为:无,除草措施,阔叶杂草控制措施和完全除草措施。评估了三种情况下的分类准确性:氮肥施用量和杂草侵染水平,仅氮肥施用量和仅杂草控制的组合。与在所有三种情况下的ANN方法相比,SVM方法导致的错误分类率非常低。使用SVM方法检测作物生长早期的压力可以帮助有效地及早应用针对特定地点的补救措施,及时进行季节性干预

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